[Dec 05, 2021] Databricks-Certified-Professional-Data-Scientist PDF Recently Updated Questions Dumps to Improve Exam Score
Databricks-Certified-Professional-Data-Scientist Dumps Full Questions with Free PDF Questions to Pass
NEW QUESTION 34
You have used k-means clustering to classify behavior of 100, 000 customers for a retail store. You decide to use household income, age, gender and yearly purchase amount as measures. You have chosen to use 8 clusters and notice that 2 clusters only have 3 customers assigned. What should you do?
- A. Increase the number of clusters
- B. Decrease the number of measures used
- C. Decrease the number of clusters
- D. Identify additional measures to add to the analysis
Answer: C
Explanation:
Explanation
kmeans uses an iterative algorithm that minimizes the sum of distances from each object to its cluster centroid, over all clusters. This algorithm moves objects between clusters until the sum cannot be decreased further. The result is a set of clusters that are as compact and well-separated as possible. You can control the details of the minimization using several optional input parameters to kmeans, including ones for the initial values of the cluster centroids, and for the maximum number of iterations.
Clustering is primarily an exploratory technique to discover hidden structures of the data: possibly as a prelude to more focused analysis or decision processes. Some specific applications of k-means are image processing^ medical and customer segmentation. Clustering is often used as a lead-in to classification. Once the clusters are identified, labels can be applied to each cluster to classify each group based on its characteristics. Marketing and sales groups use k-means to better identify customers who have similar behaviors and spending patterns.
NEW QUESTION 35
Refer to the exhibit.
You are using K-means clustering to classify customer behavior for a large retailer. You need to determine the optimum number of customer groups. You plot the within-sum-of-squares (wss) data as shown in the exhibit.
How many customer groups should you specify?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: C
NEW QUESTION 36
What is one modeling or descriptive statistical function in MADlib that is typically not provided in a standard relational database?
- A. Expected value
- B. Quantiles
- C. Linear regression
- D. Variance
Answer: C
Explanation:
Explanation
Linear regression models a linear relationship of a scalar dependent variable y to one or more explanatory independent variables x to build a model of coefficients.
NEW QUESTION 37
Suppose A, B , and C are events. The probability of A given B , relative to P(|C), is the same as the probability of A given B and C (relative to P ). That is,
- A. P(A,B|C) P(B|C) =P(C|B,C)
- B. P(A,B|C) P(B|C) =P(A|B,C)
- C. P(A,B|C) P(B|C) =P(B|A,C)
- D. P(A,B|C) P(B|C) =P(A|C,B)
Answer: B
Explanation:
Explanation
From the definition, P(A,B|C) P(B|C) =P(A,B.C)/P(C) P(B.C)/P(C) =P(A,B.C) P(B,C) =P(A|BC) This follows from the definition of conditional probability, applied twice: P(A,B)=(PA|B)P(B)
NEW QUESTION 38
You are creating a regression model with the input income, education and current debt of a customer, what could be the possible output from this model.
- A. 1 and 3 are correct
- B. expressed as a percent, that the customer will default on a loan
- C. Customer fit as acceptable or average category
- D. Customer fit as a good
- E. 2 and 3 are correct
Answer: B
Explanation:
Explanation
Regression is the process of using several inputs to produce one or more outputs. For example The input might be the income, education and current debt of a customer The output might be the probability, expressed as a percent that the customer will default on a loan. Contrast this to classification where the output is not a number, but a class.
NEW QUESTION 39
Feature Hashing approach is "SGD-based classifiers avoid the need to predetermine vector size by simply picking a reasonable size and shoehorning the training data into vectors of that size" now with large vectors or with multiple locations per feature in Feature hashing?
- A. It is hard to understand what classifier is doing
- B. Is a problem with accuracy as well as hard to understand what classifier us doing
- C. Is a problem with accuracy
- D. It is easy to understand what classifier is doing
Answer: A
Explanation:
Explanation
FEATURE HASHING
SGD-based classifiers avoid the need to predetermine vector size by simply picking a reasonable size and shoehorning the training data into vectors of that size. This approach is known as feature hashing. The shoehorning is done by picking one or more locations by using a hash of the name of the variable for continuous variables or a hash of the variable name and the category name or word for categorical, text*like, or word-like data.
This hashed feature approach has the distinct advantage of requiring less memory and one less pass through the training data, but it can make it much harder to reverse engineer vectors to determine which original feature mapped to a vector location. This is because multiple features may hash to the same location. With large vectors or with multiple locations per feature, this isn't a problem for accuracy but it can make it hard to understand what a classifier is doing.
An additional benefit of feature hashing is that the unknown and unbounded vocabularies typical of word-like variables aren't a problem.
NEW QUESTION 40
Select the correct statement which applies to Principal component analysis (PCA)
- A. Is a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables.
- B. 1 and 3 are correct
- C. Is a mathematical procedure that transforms a number of (possibly) correlated variables into a (higher) number of uncorrelated variables
- D. 1 and 2 are correct
- E. Increase the dimensionality of the data set.
Answer: A
Explanation:
Explanation
Principal component analysis (PCA) involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrected variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible.
NEW QUESTION 41
Suppose you have been given a relatively high-dimension set of independent variables and you are asked to come up with a model that predicts one of Two possible outcomes like "YES" or "NO", then which of the following technique best fit.
- A. Naive Bayes
- B. All of the above
- C. Support vector machines
- D. Random decision forests
- E. Logistic regression
Answer: B
Explanation:
Explanation
In this problem you have been given high-dimensional independent variables like yeS; nO; no English words , test results etc. and you have to predict either valid or not valid (One of two). So all of the below technique can be applied to this problem.
* Support vector machines
* Naive Bayes
* Logistic regression
* Random decision forests
NEW QUESTION 42
Select the correct algorithm of unsupervised algorithm
- A. Naive Bayes
- B. Support Vector Machines
- C. K-Nearest Neighbors
- D. K-Means
Answer: C
Explanation:
Explanation
Sup Supervised learning tasks
Classification Regression
k-Nearest Neighbors Linear
Naive Bayes Locally weighted linear
Support vector machines Ridge
Decision trees Lasso
Unsupervised learning tasks Clustering Density estimation k-Means Expectation maximization DBSCAN Parzen window
NEW QUESTION 43
You are having 1000 patients' data with the height and age. Where age in years and height in meters. You wanted to create cluster using this two attributes. You wanted to have near equal effect for both the age and height while creating the cluster. What you can do?
- A. You will be adding height with the numeric value 100
- B. You will be dividing both age and height with their respective standard deviation
- C. You will be converting each height value to centimeters
- D. You will be taking square root of height
Answer: B,C
Explanation:
Explanation
When you see the data age in years would have values like 50, 60r 70 90 years etc. And while calculating distance from centroid maximum possible value can be 90-0 and its square will be 8100.
While using heights in meter can be 2-0.5(1.5) meters and its square will be 2.25 only. So you can see age has more effect than height. Hence bringing the height on same level you can convert it into centimeters. Can bring data upto 200 centimeters and then it be more effective like square of 200 maximum.
However there is another approach is to divide the each value with its standard deviation, which will not have impact of the units e.g. age/sd of the age, which results in value without unit. This can also help in reducing the effect of units.
NEW QUESTION 44
A website is opened 3 times by a user. What is the probability of he clicks 2 times the advertisement, is best calculated by
- A. Poisson
- B. Any of the above
- C. Binomial
- D. Normal
Answer: C
Explanation:
Explanation
In a binomial distribution, only 2 parameters, namely n and p, are needed to determine the probability. Where p is the probability of success and q is the probability of failure in a binomial trial, then the expected number of successes in n trials.
This is a binomial distribution because there are only 2 possible outcomes (we get a 5 or we don't).
NEW QUESTION 45
Which of the following statement true with regards to Linear Regression Model?
- A. Ordinary Least Square is a sum of the individual distance between each point and the fitted line of regression model.
- B. In Linear model, it tries to find multiple lines which can approximate the relationship between the outcome and input variables.
- C. Ordinary Least Square is a sum of the squared individual distance between each point and the fitted line of regression model.
- D. Ordinary Least Square can be used to estimates the parameters in linear model
Answer: C,D
Explanation:
Explanation
Linear regression model are represented using the below equation
Where B(0) is intercept and B(1) is a slope. As B(0) and B(1) changes then fitted line also shifts accordingly on the plot. The purpose of the Ordinary Least Square method is to estimates these parameters B(0) and B(1).
And similarly it is a sum of squared distance between the observed point and the fitted line. Ordinary least squares (OLS) regression minimizes the sum of the squared residuals. A model fits the data well if the differences between the observed values and the model's predicted values are small and unbiased.
NEW QUESTION 46
Which of the following statement is true for the R square value in the regression model?
- A. R square can be increased by adding more variables to the model.
- B. When R square =0, all the residual are equal to 1
- C. When R square =1 , all the residuals are equal to 0
- D. R-squared never decreases upon adding more independent variables.
Answer: A,C,D
Explanation:
Explanation
R square can be made high, it means when we add more variables R-square will increase. And R-square will never decreases if you add more independent variables. Higher R square value can have lower the residuals.
NEW QUESTION 47
Select the correct statement which applies to K-Nearest Neighbors
- A. No Assumption about the data
- B. Computationally expensive
- C. Works with Numeric Values
- D. Require less memory
Answer: A,B,C
Explanation:
Explanation : k-Nearest Neighbors
Pros: High accuracy insensitive to outliers, no assumptions about data
Cons: Computationally expensive, requires a lot of memory
Works with: Numeric values, nominal values
NEW QUESTION 48
You have collected the 100's of parameters about the 1000's of websites e.g. daily hits, average time on the websites, number of unique visitors, number of returning visitors etc. Now you have find the most important parameters which can best describe a website, so which of the following technique you will use
- A. Clustering
- B. Linear Regression
- C. Logistic Regression
- D. PCA (Principal component analysis)
Answer: D
Explanation:
Explanation
Principal component analysis . or PCA, is a technique for taking a dataset that is in the form of a set of tuples representing points in a high-dimensional space and finding the dimensions along which the tuples line up best. The idea is to treat the set of tuples as a matrix M and find the eigenvectors for MMT or M T M . The matrix of these eigenvectors can be thought of as a rigid rotation in a high-dimensional space. When you apply this transformation to the original data, the axis corresponding to the principal eigenvector is the one along which the points are most "spread out,11 More precisely this axis is the one along which the variance of the data is maximized. Put another way, the points can best be viewed as lying along this axis, with small deviations from this axis.
NEW QUESTION 49
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